TestFunctionTool.httprr

Overview

This file captures an HTTP request-response trace involving the invocation of a large language model (LLM) API endpoint (https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent) to demonstrate a function tool integration scenario. It specifically illustrates the use of a function tool named sum, which computes the sum of two integers.

The purpose of this trace is to showcase interaction patterns between an LLM and a function tool, including:

• Request format with function declarations and system/user instructions.

• Model-generated function call with parameters.

• The function's execution and response encapsulated within the LLM conversation.

• Final output rendering with only the computed result.

This file serves as a concrete example of the Function Tools concept (Function Tools (80570)), demonstrating how function calls can be embedded within LLM prompts and responses to enable structured, programmatic computation via natural language interactions.

Detailed Elements

HTTP Request and Response Structure

• POST Request to LLM API

  The request includes:

  • Contents: A conversation array representing user messages and prior model responses.

    • User asks: "what is the sum of 1 + 2?"

    • System instruction enforces output to be only the function-computed result.

    • A tool declaration specifying the sum function with JSON schemas for parameters (a and b as integers) and response (sum integer).

  • Headers: Typical HTTP headers including host, user-agent, content-length, and content-type.

• Model Response

  The response includes:

  • A functionCall object indicating the model's intent to call the sum function with arguments {a:1, b:2}.

  • The LLM-generated content role is "model".

  • Metadata about token usage and model version.

Function Tool Declaration: sum

• Description: "computes the sum of two numbers"

• Name: sum

• Parameters JSON Schema:

  {
    "type": "object",
    "properties": {
      "a": {"type": "integer"},
      "b": {"type": "integer"}
    },
    "required": ["a", "b"],
    "additionalProperties": false
  }
  

• Response JSON Schema:

  {
    "type": "object",
    "properties": {
      "sum": {"type": "integer"}
    },
    "required": ["sum"],
    "additionalProperties": false
  }
  

Interaction Workflow

1. User Query: User requests the sum of 1 and 2.

2. System Instruction: Directs the LLM to output only the function-computed result.

3. Function Declaration: The sum function is declared to the LLM as a tool.

4. Model Function Call: The LLM responds by generating a functionCall object invoking sum with parameters a=1 and b=2.

5. Function Response: The function executes (outside this trace, presumably server-side or in agent context) and returns a response with the sum 3.

6. Final Model Output: The LLM outputs the final answer 3 as plain text, adhering to the system instruction.

Usage Example (Conceptual)

This trace exemplifies how an LLM agent might interact with a function tool:

// Simplified pseudo-JSON conversation
[
  {"role": "user", "content": "what is the sum of 1 + 2?"},
  {"role": "system", "content": "output ONLY the result computed by the provided function"},
  {"role": "tool", "functionDeclarations": [sum function schema]},
  {"role": "model", "functionCall": {"name": "sum", "args": {"a": 1, "b": 2}}},
  {"role": "tool", "functionResponse": {"name": "sum", "response": {"sum": 3}}},
  {"role": "model", "content": "3\n"}
]

Important Implementation Details

• Function Tool Integration: The file demonstrates how function tools are declared inline with JSON schemas, allowing LLMs to programmatically invoke typed functions with strict input/output contracts.

• System Instruction Role: The system instruction guides the LLM to produce output filtered to only the computed function result, enabling clean and precise responses without extraneous text.

• Function Invocation as Dialogue: The function call is represented as a special message part with a functionCall field, embedding the function invocation within the LLM conversation stream.

• Response Validation: The function response adheres strictly to the declared JSON schema, enabling robust validation and parsing downstream.

• Token Usage Metadata: Each response includes detailed token counts for prompt and candidate responses, useful for monitoring usage and optimizing prompt design.

Interaction With Other System Components

• LLM Agent Framework (80561): This trace is part of the interaction model where the agent framework calls an LLM model with tools (function tools) and instructions.

• Function Tools (80570): The declared sum function tool belongs to the generic Function Tools system, which wraps Go functions with JSON schema validation and integrates them into LLM agent workflows.

• Instruction Template Processing (80563) and Instruction Injection (80568): The system instruction to output only the function result is an example of instruction template processing and injection to control LLM output formatting.

• Agent Workflow Management (80558): The sequence of user input, function call invocation, and response handling fits into managed agent workflows coordinating LLM calls and tool executions.

• Telemetry and Observability (80566): Metadata such as server-timing and token counts could be used in telemetry tracking LLM and function tool call performance.

Mermaid Diagram

flowchart TD
UA[User Ask: "sum of 1 + 2?"] --> SI[System Instruction: "output ONLY result"]
SI --> TD[Tool Declaration: sum function]
TD --> LM[LLM Model]
LM --> FC["Function Call: sum(a=1, b=2)"]
FC --> FR[Function Response: sum=3]
FR --> LM2[LLM Model Output]
LM2 --> UO[User Output: "3"]
style UA fill:#fff,stroke:#000
style SI fill:#fff,stroke:#000
style TD fill:#fff,stroke:#000
style LM fill:#fff,stroke:#000
style FC fill:#fff,stroke:#000
style FR fill:#fff,stroke:#000
style LM2 fill:#fff,stroke:#000
style UO fill:#fff,stroke:#000

Summary of Key Points

• The file captures a concrete example of invoking a function tool (sum) via an LLM API.

• It demonstrates how function declarations with JSON schemas are sent to the model.

• The LLM generates a function call which is then executed and its response returned.

• The system instruction enforces that the final output is only the computed result.

• This interaction pattern is fundamental to enabling LLM agents to perform precise, schema-driven computations and tool integrations.

This file is primarily a trace/log artifact showing the exact HTTP payloads and responses involved in such a function tool scenario, valuable for debugging, testing, and understanding the integration flow.

For additional context on function tools and agent interactions, see the topics: Function Tools, LLM Integration and Agents, and Instruction Template Processing.